Breaking down the multitask paradigm in supervised deep learning
Multitask is a paradigm that covers a wide spectrum of tasks that involves the execution of ML models on multiple problems coupled with their respective datasets to achieve a goal. This paradigm is usually built based on two reasons:
- To achieve better predictive performance and generalization.
- To break down complicated goals into smaller tasks that are directly solvable using separate ML models. This reiterates the point made in the previous topic.
Let’s dive into four multitask techniques, starting with multitask pipelines.
Multitask pipelines
This variation of multitask systems revolves around realizing solutions that can’t be directly solved by using a single ML model. Breaking down highly complicated tasks into smaller tasks can allow solutions to be made with multiple ML models handling different smaller tasks. These tasks can be sequential or parallel in their paths and generally...
